
import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXModel,
		)




class 					lowerCamelCase__				:




	'''simple docstring'''




	def __init__(   self						:Tuple	,  a						:Tuple	,  a						:Tuple=1_3	,  a						:str=7	,  a						:Optional[Any]=True	,  a						:List[str]=True	,  a						:Any=True	,  a						:Any=True	,  a						:int=9_9	,  a						:List[Any]=6_4	,  a						:List[str]=5	,  a						:Union[str, Any]=4	,  a						:Tuple=3_7	,  a						:Tuple="gelu"	,  a						:Optional[int]=0.1	,  a						:Any=0.1	,  a						:Dict=5_1_2	,  a						:Any=1_6	,  a						:Tuple=2	,  a						:int=0.02	,  a						:Optional[int]=3	,  a						:str=4	,  a						:Union[str, Any]=None	,  )						->					Optional[Any]:
								__UpperCamelCase      :  Dict       =   parent
								__UpperCamelCase      :  List[str]       =   batch_size
								__UpperCamelCase      :  Union[str, Any]       =   seq_length
								__UpperCamelCase      :  Optional[Any]       =   is_training
								__UpperCamelCase      :  List[str]       =   use_input_mask
								__UpperCamelCase      :  Optional[Any]       =   use_token_type_ids
								__UpperCamelCase      :  Optional[Any]       =   use_labels
								__UpperCamelCase      :  Any       =   vocab_size
								__UpperCamelCase      :  Dict       =   hidden_size
								__UpperCamelCase      :  Union[str, Any]       =   num_hidden_layers
								__UpperCamelCase      :  Any       =   num_attention_heads
								__UpperCamelCase      :  List[str]       =   intermediate_size
								__UpperCamelCase      :  Tuple       =   hidden_act
								__UpperCamelCase      :  Any       =   hidden_dropout_prob
								__UpperCamelCase      :  Optional[Any]       =   attention_probs_dropout_prob
								__UpperCamelCase      :  List[Any]       =   max_position_embeddings
								__UpperCamelCase      :  Tuple       =   type_vocab_size
								__UpperCamelCase      :  Union[str, Any]       =   type_sequence_label_size
								__UpperCamelCase      :  str       =   initializer_range
								__UpperCamelCase      :  Tuple       =   num_labels
								__UpperCamelCase      :  List[str]       =   num_choices
								__UpperCamelCase      :  Any       =   scope
								__UpperCamelCase      :  List[str]       =   vocab_size - 1







	def   _lowerCamelCase							(   self						:Dict		)						->					Optional[int]:
								__UpperCamelCase      :  List[Any]       =   ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size		)

								__UpperCamelCase      :  List[str]       =   None
								if self.use_input_mask:
															__UpperCamelCase      :  Tuple       =   random_attention_mask([self.batch_size, self.seq_length]		)

								__UpperCamelCase      :  Any       =   None
								if self.use_labels:
															__UpperCamelCase      :  Any       =   ids_tensor([self.batch_size, self.seq_length]	,  self.num_labels		)

								__UpperCamelCase      :  Tuple       =   self.get_config()

								return config, input_ids, input_mask, token_labels







	def   _lowerCamelCase							(   self						:Dict		)						->					str:
								return GPTNeoXConfig(
								    vocab_size=self.vocab_size	,  hidden_size=self.hidden_size	,  num_hidden_layers=self.num_hidden_layers	,  num_attention_heads=self.num_attention_heads	,  intermediate_size=self.intermediate_size	,  hidden_act=self.hidden_act	,  hidden_dropout_prob=self.hidden_dropout_prob	,  attention_probs_dropout_prob=self.attention_probs_dropout_prob	,  max_position_embeddings=self.max_position_embeddings	,  type_vocab_size=self.type_vocab_size	,  is_decoder=a	,  initializer_range=self.initializer_range	,  pad_token_id=self.pad_token_id	,  )







	def   _lowerCamelCase							(   self						:Dict		)						->					Any:
								__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  List[str]       =   self.prepare_config_and_inputs()

								__UpperCamelCase      :  str       =   True

								return config, input_ids, input_mask, token_labels







	def   _lowerCamelCase							(   self						:Any	,  a						:Dict	,  a						:int	,  a						:List[Any]		)						->					Any:
								__UpperCamelCase      :  int       =   GPTNeoXModel(config=a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase      :  Dict       =   model(a	,  attention_mask=a		)
								__UpperCamelCase      :  Tuple       =   model(a		)
								self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.seq_length, self.hidden_size)		)







	def   _lowerCamelCase							(   self						:str	,  a						:List[Any]	,  a						:List[str]	,  a						:Optional[Any]		)						->					Optional[int]:
								__UpperCamelCase      :  List[Any]       =   True
								__UpperCamelCase      :  Tuple       =   GPTNeoXModel(a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase      :  str       =   model(a	,  attention_mask=a		)
								self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.seq_length, self.hidden_size)		)







	def   _lowerCamelCase							(   self						:int	,  a						:List[str]	,  a						:Dict	,  a						:Optional[int]	,  a						:Tuple		)						->					int:
								__UpperCamelCase      :  Union[str, Any]       =   GPTNeoXForCausalLM(config=a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase      :  Dict       =   model(a	,  attention_mask=a	,  labels=a		)
								self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.seq_length, self.vocab_size)		)







	def   _lowerCamelCase							(   self						:List[Any]	,  a						:Optional[int]	,  a						:Dict	,  a						:Optional[int]	,  a						:List[Any]		)						->					Dict:
								__UpperCamelCase      :  int       =   self.num_labels
								__UpperCamelCase      :  str       =   GPTNeoXForQuestionAnswering(a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase      :  str       =   model(a	,  attention_mask=a		)
								self.parent.assertEqual(result.start_logits.shape	,  (self.batch_size, self.seq_length)		)
								self.parent.assertEqual(result.end_logits.shape	,  (self.batch_size, self.seq_length)		)







	def   _lowerCamelCase							(   self						:Optional[Any]	,  a						:List[Any]	,  a						:Any	,  a						:str	,  a						:str		)						->					Optional[Any]:
								__UpperCamelCase      :  str       =   self.num_labels
								__UpperCamelCase      :  List[Any]       =   GPTNeoXForSequenceClassification(a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase      :  Optional[Any]       =   ids_tensor([self.batch_size]	,  self.type_sequence_label_size		)
								__UpperCamelCase      :  Dict       =   model(a	,  attention_mask=a	,  labels=a		)
								self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_labels)		)







	def   _lowerCamelCase							(   self						:List[Any]	,  a						:List[Any]	,  a						:List[str]	,  a						:Optional[int]	,  a						:Tuple		)						->					Any:
								__UpperCamelCase      :  List[str]       =   self.num_labels
								__UpperCamelCase      :  Union[str, Any]       =   GPTNeoXForTokenClassification(a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase      :  Optional[Any]       =   model(a	,  attention_mask=a	,  labels=a		)
								self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.seq_length, self.num_labels)		)







	def   _lowerCamelCase							(   self						:int	,  a						:List[Any]	,  a						:Any	,  a						:int		)						->					str:
								__UpperCamelCase      :  int       =   True
								__UpperCamelCase      :  Optional[int]       =   GPTNeoXForCausalLM(config=a		)
								model.to(a		)
								model.eval()

								# first forward pass
								__UpperCamelCase      :  int       =   model(a	,  attention_mask=a	,  use_cache=a		)
								__UpperCamelCase      :  str       =   outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								__UpperCamelCase      :  Union[str, Any]       =   ids_tensor((self.batch_size, 3)	,  config.vocab_size		)
								__UpperCamelCase      :  Optional[int]       =   ids_tensor((self.batch_size, 3)	,  vocab_size=2		)

								# append to next input_ids and
								__UpperCamelCase      :  Dict       =   torch.cat([input_ids, next_tokens]	,  dim=-1		)
								__UpperCamelCase      :  List[Any]       =   torch.cat([input_mask, next_mask]	,  dim=-1		)

								__UpperCamelCase      :  List[Any]       =   model(a	,  attention_mask=a	,  output_hidden_states=a		)
								__UpperCamelCase      :  Optional[Any]       =   output_from_no_past["hidden_states"][0]
								__UpperCamelCase      :  Union[str, Any]       =   model(
								    a	,  attention_mask=a	,  past_key_values=a	,  output_hidden_states=a	,  )["hidden_states"][0]

								# select random slice
								__UpperCamelCase      :  Dict       =   ids_tensor((1,)	,  output_from_past.shape[-1]		).item()
								__UpperCamelCase      :  Optional[int]       =   output_from_no_past[:, -3:, random_slice_idx].detach()
								__UpperCamelCase      :  List[Any]       =   output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(a	,  a	,  atol=1E-3		)		)







	def   _lowerCamelCase							(   self						:List[str]		)						->					Tuple:
								__UpperCamelCase      :  Optional[int]       =   self.prepare_config_and_inputs()
								__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  str       =   config_and_inputs
								__UpperCamelCase      :  str       =   {"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict




@require_torch
class 					lowerCamelCase__				(							__lowercase	,		__lowercase	,		__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	(
	    (
	        GPTNeoXModel,
	        GPTNeoXForCausalLM,
	        GPTNeoXForQuestionAnswering,
	        GPTNeoXForSequenceClassification,
	        GPTNeoXForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	_A								=	(GPTNeoXForCausalLM,) if is_torch_available() else ()
	_A								=	(
	    {
	        'feature-extraction': GPTNeoXModel,
	        'question-answering': GPTNeoXForQuestionAnswering,
	        'text-classification': GPTNeoXForSequenceClassification,
	        'text-generation': GPTNeoXForCausalLM,
	        'token-classification': GPTNeoXForTokenClassification,
	        'zero-shot': GPTNeoXForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_A								=	False
	_A								=	False
	_A								=	False
	_A								=	False







	def   _lowerCamelCase							(   self						:int		)						->					Any:
								__UpperCamelCase      :  Optional[Any]       =   GPTNeoXModelTester(self		)
								__UpperCamelCase      :  int       =   ConfigTester(self	,  config_class=a	,  hidden_size=6_4	,  num_attention_heads=8		)







	def   _lowerCamelCase							(   self						:Tuple		)						->					Any:
								self.config_tester.run_common_tests()







	def   _lowerCamelCase							(   self						:List[str]		)						->					int:
								__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  Any       =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(a	,  a	,  a		)







	def   _lowerCamelCase							(   self						:int		)						->					str:
								__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  Union[str, Any]       =   self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(a	,  a	,  a		)







	def   _lowerCamelCase							(   self						:Tuple		)						->					str:
								# This regression test was failing with PyTorch < 1.3
								__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   self.model_tester.prepare_config_and_inputs_for_decoder()

								__UpperCamelCase      :  int       =   None

								self.model_tester.create_and_check_model_as_decoder(a	,  a	,  a		)







	def   _lowerCamelCase							(   self						:List[Any]		)						->					Optional[int]:
								__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  int       =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(a	,  a	,  a		)







	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					List[str]:
								__UpperCamelCase      :  Optional[Any]       =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*a		)







	def   _lowerCamelCase							(   self						:List[Any]		)						->					List[Any]:
								__UpperCamelCase      :  Tuple       =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*a		)







	def   _lowerCamelCase							(   self						:List[Any]		)						->					List[Any]:
								__UpperCamelCase      :  List[str]       =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*a		)







	def   _lowerCamelCase							(   self						:str		)						->					List[str]:
								__UpperCamelCase      :  Optional[Any]       =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*a		)







	@unittest.skip(reason="Feed forward chunking is not implemented"		)
	def   _lowerCamelCase							(   self						:Any		)						->					Optional[int]:
								pass







	@parameterized.expand([("linear",), ("dynamic",)]		)
	def   _lowerCamelCase							(   self						:List[str]	,  a						:List[str]		)						->					Union[str, Any]:
								__UpperCamelCase  ,		__UpperCamelCase      :  Union[str, Any]       =   self.model_tester.prepare_config_and_inputs_for_common()
								__UpperCamelCase      :  Union[str, Any]       =   ids_tensor([1, 1_0]	,  config.vocab_size		)
								__UpperCamelCase      :  str       =   ids_tensor([1, int(config.max_position_embeddings * 1.5		)]	,  config.vocab_size		)

								set_seed(4_2		)  # Fixed seed at init time so the two models get the same random weights
								__UpperCamelCase      :  int       =   GPTNeoXModel(a		)
								original_model.to(a		)
								original_model.eval()
								__UpperCamelCase      :  Any       =   original_model(a		).last_hidden_state
								__UpperCamelCase      :  str       =   original_model(a		).last_hidden_state

								set_seed(4_2		)  # Fixed seed at init time so the two models get the same random weights
								__UpperCamelCase      :  Optional[Any]       =   {"type": scaling_type, "factor": 10.0}
								__UpperCamelCase      :  Optional[Any]       =   GPTNeoXModel(a		)
								scaled_model.to(a		)
								scaled_model.eval()
								__UpperCamelCase      :  Optional[int]       =   scaled_model(a		).last_hidden_state
								__UpperCamelCase      :  Optional[int]       =   scaled_model(a		).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
															self.assertTrue(torch.allclose(a	,  a	,  atol=1E-5		)		)
								else:
															self.assertFalse(torch.allclose(a	,  a	,  atol=1E-5		)		)

								# The output should be different for long inputs
								self.assertFalse(torch.allclose(a	,  a	,  atol=1E-5		)		)




@require_torch
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	@slow
	def   _lowerCamelCase							(   self						:str		)						->					Tuple:
								__UpperCamelCase      :  List[Any]       =   AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped"		)
								for checkpointing in [True, False]:
															__UpperCamelCase      :  Dict       =   GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped"		)

															if checkpointing:
																						model.gradient_checkpointing_enable()
															else:
																						model.gradient_checkpointing_disable()
															model.to(a		)

															__UpperCamelCase      :  List[str]       =   tokenizer("My favorite food is"	,  return_tensors="pt"		).to(a		)
															# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
															# See: https://github.com/huggingface/transformers/pull/24193
															__UpperCamelCase      :  Union[str, Any]       =   "My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"

															__UpperCamelCase      :  int       =   model.generate(**a	,  do_sample=a	,  max_new_tokens=2_0		)
															__UpperCamelCase      :  Tuple       =   tokenizer.batch_decode(a		)[0]

															self.assertEqual(a	,  a		)

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase    :   List[str]						=			{
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			[
		    'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'GPTBigCodeForSequenceClassification',
		    'GPTBigCodeForTokenClassification',
		    'GPTBigCodeForCausalLM',
		    'GPTBigCodeModel',
		    'GPTBigCodePreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_bigcode import (
				    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTBigCodeForCausalLM,
				    GPTBigCodeForSequenceClassification,
				    GPTBigCodeForTokenClassification,
				    GPTBigCodeModel,
				    GPTBigCodePreTrainedModel,
				)


else:
		import sys

		lowercase    :   Tuple						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)